Personal information is being continuously captured in a multitude of electronic databases. Details about health, financial status and buying habits are stored in databases managed by public and private sector organizations. These databases contain information about millions of people, which can provide valuable research, epidemiologic and business insight. For example, examining a drugstore chain's prescriptions can indicate where a flu outbreak is occurring. To extract or maximize the value contained in these databases, data custodians must often provide outside organizations access to their data. In order to protect the privacy of the people whose data is being analyzed, a data custodian will “de-identify” information before releasing it to a third-party. An important type of de-identification ensures that data cannot be traced to the person about whom it pertains, this protects against ‘identity disclosure’.
When de-identifying records, many people assume that removing names and addresses (direct identifiers) is sufficient to protect the privacy of the persons whose data is being released. The problem of de-identification involves those personal details that are not obviously identifying. These personal details, known as quasi-identifiers, include the person's age, sex, postal code, profession, ethnic origin and income (to name a few).
Data de-identification is currently a manual process. Heuristics are used to make a best guess about how to remove identifying information prior to releasing data. Manual data de-identification has resulted in several cases where individuals have been re-identified in supposedly anonymous datasets. One popular anonymization approach is k-anonymity. There have been no evaluations of the actual re-identification probability of k-anonymized data sets and datasets are being released to the public without a full understanding of the vulnerability of the dataset.
Accordingly, systems and methods that enable improved risk identification and mitigation for data sets remain highly desirable.
It will be noted that throughout the appended drawings, like features are identified by like reference numerals.